Tokoscope Python SDK

Audit, compress, and monitor your LLM token usage in 2 lines of code.

Tokoscope sits between your app and any LLM API. It tracks every call, scores your prompts for waste, compresses bloated inputs automatically, and shows you exactly where your token budget is going.

Works with OpenAI and Anthropic out of the box.

Installation

pip install tokoscope

Quick start

OpenAI

from openai import OpenAI
from tokoscope import wrap

client = wrap(OpenAI(), api_key="ts_live_...")  # get your key at app.tokoscope.com

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

print(response.choices[0].message.content)

Anthropic

from anthropic import Anthropic
from tokoscope import wrap

client = wrap(Anthropic(), api_key="ts_live_...")

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}]
)

print(response.content[0].text)

That's it. Every API call is now tracked automatically.

Real example

Original prompt: 113 tokens

Please note that it is very important that you make sure to respond
to my question. As an AI, I want you to please make sure that you
understand that I need you to help me...
What is the capital of France?

Tokoscope compressed: 8 tokens

What is the capital of France? Answer concisely.

Result: 90% token reduction. Same answer.

What you get

Once integrated, your Tokoscope dashboard shows:

• Token usage broken down by model, endpoint, and provider
• Cost per request calculated automatically using current pricing
• Waste score for every prompt
• Auto-compression — prompts with high waste scores are automatically rewritten
• Budget alerts — get notified before costs spike

Supported providers

Requirements

• Python 3.8+
• requests library (installed automatically)

License

MIT

Links

• Website
• Dashboard
• JavaScript SDK
• Contact